class_docstr: LexRank: Graph-based Centrality as Salience in Text Summarization
  Source: http://tangra.si.umich.edu/~radev/lexrank/lexrank.pdf
class_docstr_tok: ["LexRank",":","Graph-based","Centrality","as","Salience","in","Text","Summarization","Source",":","http",":","\/\/tangra.si.umich.edu\/~radev\/lexrank\/lexrank.pdf"]
code: def _compute_tf(self, sentences):
      tf_values = map(Counter, sentences)
  
      tf_metrics = []
      for sentence in tf_values:
          metrics = {}
          max_tf = self._find_tf_max(sentence)
  
          for term, tf in sentence.items():
              metrics[term] = tf / max_tf
  
          tf_metrics.append(metrics)
  
      return tf_metrics
  
file_path: sumy/sumy/summarizers/lex_rank.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  import math
  from collections import Counter
  from ._summarizer import AbstractSummarizer
label_desc: Use image node_id 6 for calling the LexRankSummarizer obj's underlying member method code with example usage: obj._compute_tf(sentences) and returns: tf_metrics